code: def syn_registration(
      moving,
      static,
      moving_affine=None,
      static_affine=None,
      step_length=0.25,
      metric="CC",
      dim=3,
      level_iters=None,
      prealign=None,
      **metric_kwargs,
  ):
      """Register a 2D/3D source image (moving) to a 2D/3D target image (static).
  
      Parameters
      ----------
      moving, static : array or nib.Nifti1Image or str.
          Either as a 2D/3D array or as a nifti image object, or as
          a string containing the full path to a nifti file.
      moving_affine, static_affine : 4x4 array, optional.
          Must be provided for `data` provided as an array. If provided together
          with Nifti1Image or str `data`, this input will over-ride the affine
          that is stored in the `data` input. Default: use the affine stored
          in `data`.
      metric : string, optional
          The metric to be optimized. One of `CC`, `EM`, `SSD`,
          Default: 'CC' => CCMetric.
      dim: int (either 2 or 3), optional
         The dimensions of the image domain. Default: 3
      level_iters : list of int, optional
          the number of iterations at each level of the Gaussian Pyramid (the
          length of the list defines the number of pyramid levels to be
          used). Default: [10, 10, 5].
      metric_kwargs : dict, optional
          Parameters for initialization of the metric object. If not provided,
          uses the default settings of each metric.
  
      Returns
      -------
      warped_moving : ndarray
          The data in `moving`, warped towards the `static` data.
      forward : ndarray (..., 3)
          The vector field describing the forward warping from the source to the
          target.
      backward : ndarray (..., 3)
          The vector field describing the backward warping from the target to the
          source.
  
      """
      level_iters = level_iters or [10, 10, 5]
  
      (
          static,
          static_affine,
          moving,
          moving_affine,
          _,
      ) = _handle_pipeline_inputs(
          moving,
          static,
          moving_affine=moving_affine,
          static_affine=static_affine,
          starting_affine=None,
      )
  
      use_metric = syn_metric_dict[metric.upper()](dim, **metric_kwargs)
  
      sdr = SymmetricDiffeomorphicRegistration(
          use_metric, level_iters, step_length=step_length
      )
  
      mapping = sdr.optimize(
          static,
          moving,
          static_grid2world=static_affine,
          moving_grid2world=moving_affine,
          prealign=prealign,
      )
  
      warped_moving = mapping.transform(moving)
      return warped_moving, mapping
  
imports: from warnings import warn
  import re
  import collections.abc
  from functools import partial
  import numbers
  import numpy
  import nibabel
  from dipy.align.metrics import CCMetric, EMMetric, SSDMetric
  from dipy.align.imwarp import SymmetricDiffeomorphicRegistration, DiffeomorphicMap
  from dipy.align.imaffine import transform_centers_of_mass, AffineMap, MutualInformationMetric, AffineRegistration
  from dipy.align.transforms import TranslationTransform3D, RigidTransform3D, RigidScalingTransform3D, RigidIsoScalingTransform3D, AffineTransform3D
  import dipy.core.gradients
  import dipy.data
  from dipy.align.streamlinear import StreamlineLinearRegistration
  from dipy.tracking.streamline import set_number_of_points
  from dipy.tracking.utils import transform_tracking_output
  from dipy.io.streamline import load_trk
  from dipy.io.utils import read_img_arr_or_path
  from dipy.io.image import load_nifti, save_nifti
label_desc: Use image node_id 2 for calling a global function with example usage: syn_registration(moving, static, moving_affine, static_affine, step_length, metric, dim, level_iters, prealign) and returns: warped_moving,  mapping